ArticleBMC bioinformatics2024
Non parametric differential network analysis: a tool for unveiling specific molecular signatures.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Differential analysis of microbial interaction networks.Briefings in bioinformatics · 2026Article
- BDDN: bayesian dynamic differential network analysis in cancer proteomics.BMC bioinformatics · 2026Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.
resultsIntroducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.
conclusionsApplying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.
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